Fixed-time synchronization for inertial Cohen-Grossberg delayed neural networks: An event-triggered approach

被引：24
作者：
Jia, Hebao [1 ]
Luo, Dongmei [2 ]
Wang, Jing [1 ]
Shen, Hao [1 ,3 ]
机构：
[1] Anhui Univ Technol, Sch Elect & Informat Engn, Maanshan 243032, Peoples R China
[2] Anhui Univ Technol, Sch Math & Phys, Maanshan 243032, Peoples R China
[3] Anhui Univ Technol, AnHui Prov Key Lab Special Heavy Load Robot, Maanshan 243032, Peoples R China
来源：
KNOWLEDGE-BASED SYSTEMS | 2022年 / 250卷
基金：
中国国家自然科学基金;
关键词：
Cohen-Grossberg neural networks; Event-triggered mechanism; Fixed-time synchronization; Inertial neural networks; GLOBAL EXPONENTIAL STABILITY; STABILIZATION; DESIGN;
D O I：
10.1016/j.knosys.2022.109104
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper addresses the fixed-time synchronization problem for inertial Cohen-Grossberg neural networks with external disturbances and time-varying delays. Compared with some existing works about fixed-time synchronization control methods, a novel controller is constructed with a dynamic exponential term, which can contain the two exponents. Moreover, taking into account the increase of network complexity as well as a huge quantity of data transmission, an event-triggered mechanism is introduced to effectively utilize the limited network bandwidth. By employing the variable transformation method, differential mean value theorem, and the fixed-time stability theory, some sufficient conditions ensuring the fixed-time synchronization of inertial Cohen-Grossberg neural networks are established. Finally, two numerical examples are given to illustrate the validity of the obtained results. (C) 2022 Elsevier B.V. All rights reserved.
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页数：9
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